Systems and methods for comprehensive analysis of molecular profiles across multiple tumor and germline exomes

ABSTRACT

Omics patient data are analyzed using sequences or diff objects of tumor and matched normal tissue to identify patient and disease specific mutations, using transcriptomic data to identify expression levels of the mutated genes, and pathway analysis based on the so obtained omic data to identify specific pathway characteristics for the diseased tissue. Most notably, many different tumors have shared pathway characteristics, and identification of a pathway characteristic of a tumor may thus indicate effective treatment options ordinarily not considered when tumor analysis is based on anatomical tumor type only.

This application claims the benefit of priority to U.S. provisionalapplication having Ser. No. 62/005,766, filed 30 May 2014, and which isincorporated by reference herein.

FIELD OF THE INVENTION

The field of the invention is computational omics, especially as itrelates to analysis of molecular profiles across a large number of tumorand germline exomes from multiple patient and tumor samples.

BACKGROUND OF THE INVENTION

The background description includes information that may be useful inunderstanding the present invention. It is not an admission that any ofthe information provided herein is prior art or relevant to thepresently claimed invention, or that any publication specifically orimplicitly referenced is prior art.

While the clinical world is familiar with genomic assays targeted to alimited number of mutations as a means to derive molecular insight totherapies, the power to deliver more comprehensive, non-assumptive, andstochastic molecular analysis is sorely needed to guide treatmentdecisions that are unbiased to traditional tissue-by-tissue anatomicalassignment of therapeutics, or a priori assumptions that a few hundredDNA mutations are drivers of cancer. Indeed, most clinicians today arechallenged by a deluge of rapidly advancing science with which itbecomes increasingly difficult to keep pace. In this era of personalizedmedicine, there are nearly 800 drugs in development targeted againstspecific protein targets driving the growth of the tumor. This cognitiveoverload may have significant consequences in decision making inlife-threatening diseases as complex as cancer.

Today the approach most widely used by oncologists to guide treatmentselection of drugs that are targeted against altered proteins is toidentify gene DNA mutations in tumor samples deploying panels of fewerthan 500 “actionable” genes. Such actionable genes are typicallyidentified from large-scale studies of various cancers (see e.g., NatureGenetics 45, 1127-1133 (2013)). All publications and applications hereinare incorporated by reference to the same extent as if each individualpublication or patent application were specifically and individuallyindicated to be incorporated by reference. Where a definition or use ofa term in an incorporated reference is inconsistent or contrary to thedefinition of that term provided herein, the definition of that termprovided herein applies and the definition of that term in the referencedoes not apply.

Unfortunately, the current reliance on genotyping of tumor samples todrive treatment decisions is largely based on the assumption thatidentification of mutated DNA routinely translates downstream (from “DNAto protein expression”) to an alteration in the underlying proteinpathways that are targeted by the therapy to be selected, and theseidentified DNA mutations are thus nominated as clinically actionable.However, exclusive analysis of genetic mutations in tumor genomes failsto take into account whether or not the mutated genes are transcribed atall, whether changes in the genome are variants or disease-drivers,and/or what the functional context of such mutations are, and whether ornot compensatory mechanisms exists in a cell affected by such mutation.

Therefore, analysis of selected mutations with disregard of the abovedrawbacks will likely lead to various false-positive, false negative,and non-relevant results that in turn may misdirect treatment of apatient. Therefore, there remains a need for improved systems andmethods for comprehensive analysis of molecular profiles.

SUMMARY OF THE INVENTION

The inventive subject matter is drawn to systems and methods of omicsanalysis in which shared pathway characteristics are obtained fromvarious distinct tumor samples. Most preferably, omics analysis includesanalysis of tumor and matched normal tissue to identify patient andtumor specific changes, which is further refined using transcriptomicsdata. Based on such analysis, a treatment recommendation is thenprepared that is typically independent of the anatomical tumor type butthat takes into account a molecular signature characteristic of sharedpathway characteristics.

In one aspect of the inventive subject matter, the inventors contemplatea method of identifying a molecular signature for a tumor cell thatincludes a step of using an analysis engine to receive a plurality ofdata sets from a respective plurality of patients, wherein at least two(or at least three, or at least five) of the plurality of patients arediagnosed with different tumors, and wherein each data set isrepresentative of genomics information from tumor and matched normalcells. In another step, the analysis engine receives transcriptomicsinformation for the at least two patients, and in yet another step, theanalysis engine identifies shared pathway characteristics among thetumor cells of the at least two patients using the genomics informationand the transcriptomics information. In a still further step, theanalysis engine is then used to assign, on the basis of the sharedpathway characteristics, a molecular signature to the tumor cells,wherein the molecular signature is assigned independently of ananatomical tumor type, and a patient record is then generated or updatedusing the molecular signature.

While not limiting to the inventive subject matter, it is generallycontemplated that the data sets are in a BAMBAM format, a SAMBAM format,a FASTQ format, or a FASTA format, and it is typically preferred thatthe data sets are BAMBAM diff objects. Therefore, in furthercontemplated aspects, the data sets will preferably comprise mutationinformation, copy number information, insertion information, deletioninformation, orientation information and/or breakpoint information.

With respect to the genomics information it is contemplated that suchinformation may be whole genome sequencing information or exomesequencing information, and that the transcriptomics informationcomprises information on transcription level and/or sequenceinformation. Most typically, the transcriptomics information will coverat least 50% (or at least 80%) of all exomes in the genomics informationfrom the tumor cells. Furthermore, it is contemplated that thetranscriptomics information is used in the step of identifying to inferreduced or absence of function of a protein encoded by a mutated gene.

Therefore, the inventors contemplate that the shared pathwaycharacteristics will include a constitutively activated pathway, afunctionally impaired pathway, and a dysregulated pathway, and/or thatthe shared pathway characteristics may be characterized by a mutatednon-functional protein, mutated dysfunctional protein, an overexpressedprotein, or an under-expressed protein. In still further preferredaspects, the step of identifying is performed using PARADIGM or otherpathway-centric method of analysis.

Additionally, it is contemplated that the molecular signature comprisesinformation about one or more pathway elements, and especially drugidentification and type of interaction with the one or more pathwayelements. Therefore, it should be appreciated that the patient recordmay also include a treatment recommendation based on the molecularsignature of the tumor cells (e.g., treatment recommendation for a firstpatient with a first tumor may be based on shared pathwaycharacteristics with a second patient with a distinct second tumor).

Various objects, features, aspects and advantages of the inventivesubject matter will become more apparent from the following detaileddescription of preferred embodiments, along with the accompanyingdrawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a graph illustrating frequency distribution of ‘actionablegenes’ for selected tumors.

FIG. 2 is a graph correlating RNA expression levels of mutated genomicDNA for selected tumors.

FIG. 3 is an exemplary graph depicting principal component analysis forselected oncogenes in selected tumors.

FIG. 4 is a an exemplary graph depicting survival times as a function ofgenomic rearrangements.

FIG. 5 is a chart depicting an exemplary breakpoint analysis forselected tumors.

FIG. 6 is a graph depicting pathway activity clusters based on corepathways that are over- or under-activated.

FIG. 7 is a graph depicting pathway activities clustered across varioustumor types.

FIG. 8 is an exemplary graph depicting mutation distribution for varioustumor types.

DETAILED DESCRIPTION

The following description includes information that may be useful inunderstanding the present invention. It is not an admission that any ofthe information provided herein is prior art or relevant to thepresently claimed invention, or that any publication specifically orimplicitly referenced is prior art.

The inventive subject matter provides apparatus, systems, and methodsfor improved omics analysis of various tumors. More specifically, theinventors discovered that omics data analysis can be significantlyimproved by first identifying patient and tumor relevant changes in thegenome, typically via comparison of tumor and matched normal samples.Once such differences are ascertained, further transcriptomic data ofthe same patient are used to identify whether the changed sequences areexpressed in the tumor. The so obtained patient data are then subjectedto pathway analysis to identify pathway characteristics of the tumor,and particularly shared pathway characteristics of the tumor withvarious other types of tumors. As should be readily appreciated, sharedpathway characteristics may be employed to inform treatment using one ormore treatment modalities from anatomically unrelated tumors that wouldotherwise not have been identified. Viewed from a different perspective,different tumor types share pathway characteristics irrespective of theanatomical tumor type, and the knowledge of shared pathwaycharacteristics with respective molecular signatures may identify drugtreatment strategies that had not been appreciated for a particulartumor type.

Consequently, in one aspect of the inventive subject matter, theinventors contemplate a method of identifying a molecular signature fora tumor cell, and especially a molecular signature of a cell signalingpathway. Most typically, identification and analysis is performed usinga fully integrated, cloud-based, supercomputer-driven, genomic, andtranscriptomic analytic engine. It should be noted that any languagedirected to a computer should be read to include any suitablecombination of computing devices, including servers, interfaces,systems, databases, agents, peers, engines, controllers, or other typesof computing devices operating individually or collectively. One shouldappreciate the computing devices comprise a processor configured toexecute software instructions stored on a tangible, non-transitorycomputer readable storage medium (e.g., hard drive, solid state drive,RAM, flash, ROM, etc.). The software instructions preferably configurethe computing device to provide the roles, responsibilities, or otherfunctionality as discussed below with respect to the disclosedapparatus. In especially preferred embodiments, the various servers,systems, databases, or interfaces exchange data using standardizedprotocols or algorithms, possibly based on HTTP, HTTPS, AES,public-private key exchanges, web service APIs, known financialtransaction protocols, or other electronic information exchangingmethods. Data exchanges preferably are conducted over a packet-switchednetwork, the Internet, LAN, WAN, VPN, or other type of packet switchednetwork.

In especially preferred methods, an analysis engine receives a pluralityof data sets from a respective plurality of patients, wherein at leasttwo of the plurality of patients are diagnosed with different tumors,and wherein each data set is representative of genomics information fromtumor and matched normal cells. In a further step, the analysis enginereceives transcriptomics information for the at least two patients andidentifies shared pathway characteristics among the tumor cells of theat least two patients using the genomics information and thetranscriptomics information (of course, it should be noted that sharedpathway characteristics may also be identified only for a single patientsample while pathway characteristics of other tumors may be obtainedfrom a pathway database). In yet another step, the analysis engine isthen used to assign, on the basis of the shared pathway characteristics,a molecular signature to the tumor cells, wherein the molecularsignature is assigned independently (i.e., in an agnostic manner) of ananatomical tumor type. In a still further step, a patient record may begenerated or updated using the molecular signature.

With respect to the data sets from the plurality of patients it iscontemplated that the type of data sets may vary considerably and thatnumerous types of data sets are deemed suitable for use herein.Therefore, data sets may include unprocessed or processed data sets, andexemplary data sets include those having BAMBAM format, SAMBAM format,FASTQ format, or FASTA format. However, it is especially preferred thatthe data sets are provided in BAMBAM format or as BAMBAM diff objects(see e.g., US2012/0059670A1 and US2012/0066001A1). Therefore, and viewedfrom another perspective, it should be noted that the data sets arereflective of a tumor and a matched normal sample of the same patient toso obtain patient and tumor specific information. Thus, genetic germline alterations not giving rise to the tumor (e.g., silent mutation,SNP, etc.) can be excluded. Of course, it should be recognized that thetumor sample may be from an initial tumor, from the tumor upon start oftreatment, from a recurrent tumor or metastatic site, etc. In mostcases, the matched normal sample of the patient may be blood, ornon-diseased tissue from the same tissue type as the tumor.

It should also be noted that the data sets may be streamed from a dataset generating device (e.g., sequencer, qPCR machine, etc.) or providedfrom a data base storing the data sets. For example, suitable data setsmay be derived from a BAM server (e.g., as described in US2012/0059670A1and US2012/0066001A1) and/or a pathway analysis engine (e.g., asdescribed in WO2011/139345A2 and WO2013/062505A1). Such is particularlytrue where the data sets from a tumor and matched normal sample are notderived from the patient. Thus, at least some of the data sets may beindependently stored and provided, and analysis may be performed on anewly obtained patient sample (e.g., within one week of obtainingpatient tissue samples) using data sets from the patient's tumor andmatched normal sample and previously stored tumor and matched normalsample not derived from the patient.

With further respect to the data sets it is noted that the data setsfrom all tumors are in a format that allows ready comparison withoutfurther conversion and/or processing. Thus, the data sets willpreferably comprise mutation information, methylation statusinformation, copy number information, insertion/deletion information,orientation information, and/or breakpoint information specific to thetumor and the patient. It is still further contemplated that the dataset is representative of at least a portion of the entire genome, andmost typically the whole genome. Therefore, the data sets are preferablyprepared form whole genome sequencing covering the entire genome (or atleast 50%, or at least 70%, or at least 90% of the entire genome).Alternatively, exome sequencing is also contemplated, and in most casesit is contemplated that at least 50%, more typically at least 70, andmost typically at least 90% of the entire exome is sequenced.

Moreover, and with respect to the origin of the data sets it should beappreciated that numerous non-patient tumor data are used. Therefore, itis contemplated that for data sets other than a patient data set will bederived from at least two different tumors, and more preferably from atleast three, or at least five different tumor types to identify sharedpathway characteristics. Data sets from different tumor types can beobtained from different patient samples as such samples are available(e.g., from a hospital, clinical trial, epidemiological study, etc.)and/or can be provided from previously acquired analyses or data. Forexample, the TCGA provides a good sample of well-characterized omicinformation useful to prepare data sets suitable for use herein andTable 1 below exemplarily illustrates data used in the present analysis.

Sex Tumor Grade Median Survival Tissue Subtype N Age M F G1 G2 G3 G4 GXGB ? (months) Breast ER− 16 50.3 (58.9 ) 0 16 0 0 0 0 0 0 16 26.04Lobular ER+ 148 62.0 (61.9) 0 146 0 0 0 0 0 0 148 146.48 Lung Squamous366 66.5 (67.6) 287 99 0 0 0 0 0 0 366 46.76 Rectal 96 67.7 (66.0) 53 400 0 0 0 0 0 90 51.98 Breast Ductal ER− 225 56.3 (56.7) 0 225 0 0 0 0 0 0225 100.70 ER+ 516 59.5 (58.2) 9 507 0 0 0 0 0 0 516 113.62 Glioblastoma354 60.8 (60.1) 222 132 0 0 0 0 0 0 354 13.94 Stomach MSI 55 69.0 (68.6)46 42 0 30 55 0 0 0 0 26.47 MS3 160 67.0 (65.9) 105 55 0 54 99 0 4 0 072.23 AML 4 60.1 (50.2) 3 1 0 0 0 0 0 0 4 47.05 Low Grade Glioma 13841.1 (42.8) 72 66 0 68 70 0 0 0 0 78.21 Head & Neck HPV− 246 62.4 (62.7)168 78 26 159 55 0 0 0 0 47.97 HPV+ 56 59.4 (69.6) 46 8 1 31 19 2 3 0 052.31 Bladder 118 68.8 (67.1) 56 32 0 0 0 0 0 0 118 19.50 Uterine 29963.6 (64.0) 0 299 0 0 0 0 0 0 299 NA Prostate 178 61.3 (60.8) 178 0 0 00 0 0 0 178 NA Lung Adeno. 369 66.9 (66.7) 171 195 0 0 0 0 0 0 369 40.00Colon MSS 144 68.2 (66.8) 62 62 0 0 0 0 0 0 144 NA MSI 76 73.1 (69.0) 3348 0 0 0 0 0 0 70 NA Thyroid 419 46.9 (47.6) 104 315 0 0 0 0 0 0 419 NAKidney Clear Cell 325 60.4 (60.4) 209 116 0 142 126 47 2 0 8 90.45Kidney Chromophobe 60 48.3 (60.0) 29 21 0 0 0 0 0 0 50 NA Ovarian 33859.5 (60.4) 0 336 3 37 285 0 8 1 2 43.66 Melanoma 301 66.8 (67.3) 192109 0 0 0 0 0 0 301 98.40 Pancreatic 4 ? ? ? 0 0 0 0 0 0 4 NA Total 5052

With reference to the TCGA data it was further observed that differenttumor types had multiple mutations in multiple genes. As such, it isapparent that simple targeting of an individual druggable target is inmost circumstances not a viable option. Indeed, FIG. 1 exemplarilyillustrates the predicament for conventional singular moleculardiagnostics where various tumor types are shown with their respectivenumerical distribution of potentially actionable genes. As is readilyapparent from FIG. 1, there was a multitude of actionable genes, notjust single mutation, in almost all tumors. Thus, it should beappreciated that the analysis and treatment of a tumor requiresconsideration of more than one changed gene. In addition, it haspreviously not yet been appreciated that not all of the mutated genesare indeed expressed, and with that may or may not lead to actionable ordruggable protein targets as is exemplarily depicted in FIG. 2.

As can be taken from FIG. 2, selected mutations in certain tumors werenot or only weakly expressed (i.e., transcribed into RNA, see lowerbox). Consequently, pharmaceutical intervention targeting such mutantproteins (e.g., targeting BRAF V600 in glioblastoma) are not expected toimpact the tumor in a significant manner. Conversely, certain othermutated proteins will provide an attractive target due to their veryhigh rate of expression (e.g., by targeting BRAF V600 in melanoma).Thus, it should be appreciated that the same mutated protein may be asuitable target in some cancers or patients and an entirely unsuitabletarget in others. Viewed from a different perspective, genomicsinformation without consideration of transcriptomics data will lackdetail needed to guide treatment decisions.

In particularly preferred aspects, transcription information is obtainedto cover at least 50%, or at least 70, or at least 80, or at least 90%of all exomes in the genomics information from the tumor cells. Thus, itis contemplated that transcripts of a tumor cell or tissue may also beanalyzed for their quantity (and optionally also for sequenceinformation to identify RNA editing and/or RNA splicing). Such analysismay include threshold values that are typically user defined as furtherdescribed in copending US provisional application with the Ser. No.62/162,530, filed 15 May 2015.

In addition to the lack of consideration of transcriptomics data, thefunctional impact of a mutation within a cell signaling network has notbeen appreciated in most of heretofore known systems and methods,especially where multiple mutations are present in multiple genesassociated with a tumor. To overcome such shortcoming, the inventorsused the patient and tumor specific mutation information and associatedexpression levels in an analysis of cell signaling pathways to therebyobtain information on pathway usage and compensation where a pathwayfunction was compromised. Therefore, it is noted that thetranscriptomics information is preferably used to infer reduced orabsence of function of a protein encoded by a mutated gene, and withthat influence on a particular pathway.

While various pathway analytical tools are known in the art, theinventors especially contemplate use of dynamic pathway maps in whichpathways are expressed as probabilistic pathway model. For example,pathway analyses may be performed using PARADIGM, as described inWO2011/139345, WO2013/062505, WO2014/059036, or WO2014/193982, using thedata sets and transcriptomics information to so arrive at the particularpathways usage of a specific tumor. As will be readily appreciated,where multiple data sets from multiple patients having distinct tumorsas employed, the analysis engine will be able to identify for each tumorparticular pathway characteristics with a molecular signature of thetumor cells. For example, the analysis engine may identify sharedpathway characteristics among multiple tumor types where such sharedcharacteristics may include a constitutively activated pathway, afunctionally impaired pathway, and a dysregulated pathway. Such sharedpathway may be characterized or due to a variety of factors andexemplary factors leading to a particular pathway characteristic includea mutated non-functional protein, mutated dysfunctional protein, anoverexpressed protein, or an underexpressed protein in a pathway, etc.Of course, it should be noted that at least some of the pathwaycharacteristics may be previously determined and stored in a data baseor that at least some of the pathway characteristics may also bedetermined de novo. Therefore, it should be recognized that new patientdata may be compared against already obtained data from a database.

Among other benefits of integrated genomics, transcriptomics, andpathway analysis for multiple tumor types of multiple patients, itshould be appreciated various subsequent analyses are now possible togroup or classify certain molecular events into otherwise not observablecategories. For example, as is illustrated in FIG. 3, a principalcomponent analysis of various expressed mutated oncogenes from differenttumors can be performed to so associate a plurality of specificmutations with a plurality of different tumors. Likewise, breakpointanalysis over different tumors can be associated with prognostic outcomeas exemplarily shown in FIG. 4, or breakpoint frequency and distributioncan be associated with different tumors as exemplarily shown in FIG. 5.

Most notably, and as exemplarily shown in FIG. 6, pathway analysis onthe basis of genomics and transcriptomics information may serve toidentify certain shared molecular signatures common to a variety ofdifferent tumors. Thus, it should be recognized that a tumor may beclassified as belonging to a class of tumors that are characterized byspecific shared pathway characteristics. With further reference to FIG.6, it is noted that the tumors of Table 1 together with genomics andtranscriptomics information were stratified into six distinct classesindependent of anatomical location. Here, common classes for differenttumors were defined by activation or inhibition of selected signalingpathways (e.g., over-activation of myc transcription and inhibition ofNOTCH signaling), which is entirely independent from a classificationbased on anatomical tumor type (classified as pancreatic tumor, breastductal tumor, etc.).

FIG. 7 exemplarily illustrates a different perspective of the findingsof FIG. 6 where the tumor classification is expressed as clusters perFIG. 6. Here, it is readily apparent that entirely unrelated tumors(e.g., uterine, rectal, lung adeno.) can be classified according tospecific signaling pathways characteristics having specific molecularsignatures. For example, the molecular signature may compriseinformation about one or more pathway elements within a pathway (e.g.,Ras, Raf, MEK, Myc). As such, where a tumor shares a common pathwaycharacteristic with one or more common molecular signatures with anotherunrelated tumor, the tumor may in fact be treatable using treatmentmodalities know for the unrelated tumor. Most typically, the molecularsignature information may include a drug identification (e.g., where Rasis mutated and overexpressed, drug information may include suitable Rasinhibitors) and/or a type of interaction with the one or more pathwayelements (e.g., where Hec1 is mutated and overactive, drug informationmay include suitable Hec1/Nek inhibitors). Therefore, and viewed fromanother perspective, a patient tumor may be characterized as belongingto a specific class where that class is defined as having unrelated anddistinct members (tumors) sharing common pathwaycharacteristics/molecular signatures within a pathway. Based on the soestablished classification, treatment options may be selected based ontreatment options known or available for the unrelated and distinctmembers. It should be appreciated that the treatment option may target amutated element of a particular pathway, but also that the treatmentoption may target a non-mutated element of another pathway thatcompensates for a defect in a pathway in which a mutated element isdisposed.

In another manner of classification, the inventors contemplate thatselected pathways and/or pathway elements may be analyzed from amultiple different tumors as is exemplarily shown in FIG. 8. Here,selected pathway elements (e.g., tumor suppressors and oncogenes) areplotted against different tumors, which provides a rapid identificationof shared pathway characteristics and molecular signatures common tomultiple tumors. For example, the KRAS G12 mutant is associated withuterine, rectal, and colon cancers, while mutated APC is associated withcolon adenocarcinoma and rectal cancers.

Therefore, the inventors contemplate that a patient record willtypically include one or more treatment recommendations based on themolecular signature of the tumor cells (and with that based on theshared pathway characteristics with other unrelated tumors). In otherwords, a treatment recommendation for a first patient with a first tumormay be based on a shared pathway characteristics with a second patientwith a distinct second tumor.

As used in the description herein and throughout the claims that follow,the meaning of “a,” “an,” and “the” includes plural reference unless thecontext clearly dictates otherwise. Also, as used in the descriptionherein, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise. Moreover, as used herein, and unless thecontext dictates otherwise, the term “coupled to” is intended to includeboth direct coupling (in which two elements that are coupled to eachother contact each other) and indirect coupling (in which at least oneadditional element is located between the two elements). Therefore, theterms “coupled to” and “coupled with” are used synonymously. Moreover,all methods described herein can be performed in any suitable orderunless otherwise indicated herein or otherwise clearly contradicted bycontext. The use of any and all examples, or exemplary language (e.g.“such as”) provided with respect to certain embodiments herein isintended merely to better illuminate the invention and does not pose alimitation on the scope of the invention otherwise claimed. No languagein the specification should be construed as indicating any non-claimedelement essential to the practice of the invention.

It should be apparent to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein. The inventive subjectmatter, therefore, is not to be restricted except in the scope of theappended claims. Moreover, in interpreting both the specification andthe claims, all terms should be interpreted in the broadest possiblemanner consistent with the context. In particular, the terms “comprises”and “comprising” should be interpreted as referring to elements,components, or steps in a non-exclusive manner, indicating that thereferenced elements, components, or steps may be present, or utilized,or combined with other elements, components, or steps that are notexpressly referenced. Where the specification claims refers to at leastone of something selected from the group consisting of A, B, C . . . andN, the text should be interpreted as requiring only one element from thegroup, not A plus N, or B plus N, etc.

What is claimed is:
 1. A computer-assisted method of treating a cancerpatient by identifying a molecular signature for a tumor cell,comprising: receiving a plurality of data sets from a respectiveplurality of patients, wherein at least two of the plurality of patientsare diagnosed with first and second tumors, respectively, wherein thefirst and second tumors are different anatomical tumors that originatefrom different anatomical organs of the patients; wherein each data setis representative of genomics information from tumor and matched normalcells; receiving transcriptomics information for the at least twopatients; identifying shared pathway characteristics among the tumorcells of the first and second different anatomical tumors using thegenomics information and the transcriptomics information; assigning, onthe basis of the shared pathway characteristics, a molecular signatureto the tumor cells of the first and second different anatomical tumors,wherein the molecular signature is assigned independently of ananatomical tumor type; identifying a shared molecular signature betweenthe first and second tumors, wherein the shared molecular signaturecomprises a shared pathway element between the first and second tumorsthat is a druggable target; and treating the cancer patient having thesecond tumor with a treatment that is known effective to treat the firsttumor, wherein the treatment targets the shared pathway element, whereinthe shared pathway element is a mutated pathway element, and thetreatment targets the shared pathway element by targeting anotherpathway element that compensates for a defect of the mutated pathwayelement.
 2. The method of claim 1, wherein the plurality of data setsare in a BAMBAM format, a SAMBAM format, a FASTQ format, or a FASTAformat.
 3. The method of claim 1, wherein the plurality of data sets areBAMBAM diff objects.
 4. The method of claim 1, wherein the plurality ofdata sets comprise mutation information, copy number information,insertion information, deletion information, orientation informationand/or breakpoint information.
 5. The method of claim 1, wherein atleast three of the plurality of patients are diagnosed with differentanatomical tumors.
 6. The method of claim 1, wherein at least five ofthe plurality of patients are diagnosed with different anatomicaltumors.
 7. The method of claim 1, wherein the genomics information iswhole genome sequencing information.
 8. The method of claim 1, whereinthe genomics information is exome sequencing information.
 9. The methodof claim 1, wherein the transcriptomics information comprisesinformation on transcription level.
 10. The method of claim 1, whereinthe transcriptomics information comprises information on RNA sequence.11. The method of claim 1, wherein the transcriptomics informationcovers at least 50% of all exomes in the genomics information from thetumor cells.
 12. The method of claim 1, wherein the transcriptomicsinformation covers at least 80% of all exomes in the genomicsinformation from the tumor cells.
 13. The method of claim 1, wherein theshared pathway characteristics are selected from the group consisting ofa constitutively activated pathway, a functionally impaired pathway, anda dysregulated pathway.
 14. The method of claim 1, wherein the sharedpathway characteristics are characterized by a mutated non-functionalprotein, mutated dysfunctional protein, an overexpressed protein, or anunderexpressed protein in a pathway.
 15. The method of claim 1, whereinthe transcriptomics information is used in the step of identifyingshared pathway characteristics to infer reduced or absence of functionof a protein encoded by a mutated gene.
 16. The method of claim 1,wherein the step of identifying shared pathway characteristics isperformed using a probabilistic pathway model.
 17. The method of claim1, wherein compensating for a defect of the mutated pathway elementcomprises inhibiting a function of the mutated pathway.
 18. The methodof claim 17, wherein the information of the molecular signaturecomprises drug identification and type of interaction with the one ormore pathway elements.
 19. The method of claim 1, further comprisingupdating or generating a patient record using the shared molecularsignature.